🤖 AI Summary
This paper addresses the fundamental question of whether algorithms can genuinely be neutral, examining the ontological nature, feasibility, and normative significance of algorithmic neutrality.
Method: It constructs the first cross-model theoretical framework integrating computational philosophy, formal modeling, and counterfactual reasoning to rigorously define neutrality as a structural, falsifiable benchmark independent of fairness.
Contribution/Results: The work introduces the novel principle that “absence of neutrality implies presence of bias,” thereby clarifying the logical entailment relationship between neutrality and bias. By reconceptualizing neutrality as a prerequisite for algorithm design—rather than a post-hoc property—the study advances a paradigm shift in algorithmic governance: from reactive fairness remediation to proactive, neutrality-grounded system design. This provides a foundational basis for systematic bias identification, causal attribution, and structural correction in algorithmic systems.
📝 Abstract
Algorithms wield increasing control over our lives: over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?